CN1279448C - Monitoring equipment and monitored equipment - Google Patents

Abstract

A monitoring system for monitoring the state of a monitoring object apparatus so as to detect a foretoken of failure. A host computer (100) of a manufacturer/dealer of a clinical check apparatus (200) receives state data of respective parts from parts sensors (203a, 203b, . . . ) of the clinical check apparatus (200) of a user and the received state data is compared to a prediction condition stored in a condition storage block (104) in a state monitoring block (105), thereby detecting a foretoken of failure. The prediction condition includes a condition created in accordance with service life of each of the parts and a condition created upon occurrence of a failure in accordance with transition of the state data prior to the occurrence of the failure.

Description

Monitoring equipment and monitored equipment

technical field

The present invention relates to a system in which a monitoring device remotely controls a plurality of devices to be monitored through a communication network in order to predict failure and the like of each monitored device.

Background technique

Usually, for various testing equipment, including clinical testing equipment, machine tools or automobiles, or various types of other equipment that requires precise operation, after the equipment is delivered to use, the manufacturer/distributor of these equipment will continue to perform maintenance to Maintain equipment performance and ensure safety. In particular, failure of the equipment prevents smooth operations by the operator of the equipment and may also endanger the safety of the operator. Therefore, from the viewpoint of improving customer satisfaction, suppressing increases in maintenance costs, and ensuring safety, it is very important to prevent failures by regularly inspecting equipment and replacing consumable parts in advance.

By convention, the timing of periodic inspections and parts replacement is usually set appropriately by people based on past rules of thumb. Alternatively, these times may be set based on the data of the service life of each component.

However, in the case where the timing of inspection/replacement is set based only on the above-mentioned past experience or the service life data of each part, etc., there may be differences in service life even between parts of the same type due to differences in usage conditions, etc. are different, which leads to the following problems. That is, contrary to expectations, there is a possibility that a failure may occur before the time for an inspection/replacement, or, conversely, that a component may have to be replaced before it has a sufficient lifespan, which in turn causes an increase in maintenance costs. increase, and this is precisely detrimental.

Contents of the invention

In view of the above, one of the objects of the present invention is to provide a monitoring system, which monitors the status of a monitored device through a communication network, and can realize maintenance and inspection of the monitored device at an appropriate time to detect abnormalities indication of the condition, allowing the equipment to achieve a higher uptime rate while inhibiting the increase in maintenance costs.

In order to achieve the above object, a monitoring device according to the present invention includes: a state data receiving part, which receives state data representing the state of the monitored device from a monitored device through a communication network; a condition storage part, which stores prediction conditions; and a state A monitoring section that compares state data with predicted conditions in the condition storage section to predict anomalies. In this device, the predictive condition includes a predictive condition generated based on a change pattern indicating an abnormal precursor in the state data before the time point when the abnormal situation occurs in the monitored device.

In this configuration, as for the status data received from the monitored equipment, after the data are continuously received, it is compared with the predicted condition stored in the condition storage section, so that abnormality of the monitored equipment is predicted. A condition based on a transition of state data before the point in time when the abnormality occurred can be used as one of the conditions constituting the prediction condition, so that the occurrence of the abnormal situation can be more accurately predicted. Therefore, it is possible to realize a monitoring device that allows a monitored device to achieve a higher uptime rate than conventionally in a case where the timing of maintenance inspection is appropriately set based on the service life data of components, etc. ; and an increase in maintenance costs resulting from maintenance services performed by manufacturers/distributors, etc. of monitored equipment can be suppressed.

Preferably, in the monitoring device, the predictive condition thus generated is based on transitions of the status data having a common pattern of changes among the transitions of the status data observed in at least two monitored devices in which a common abnormal situation occurs conditions that arise.

For example, when an occasional exception occurs, the pattern of change in status data obtained before the exception does not necessarily provide a reliable indication of the exception. Using prediction conditions based on such variation patterns may instead lead to a decrease in the accuracy of predicting anomalies. Therefore, as in the case of the above-described configuration, prediction conditions are generated based on common change patterns obtained when common abnormalities occur, and thus it is possible to set useless prediction conditions for detecting signs of abnormality. This allows for more accurate detection of anomalies.

Preferably, the monitoring device further includes: a data storage part accumulating state data received from the monitored device; and an abnormality analysis part using the state data accumulated in the data storage part to analyze the The transition of generates a prediction condition and stores the prediction condition into the condition storage section.

According to this configuration, the abnormality analysis section generates a prediction condition based on the status data received from the monitored device in actual operation by the user, and stores the prediction condition in the condition storage section. Therefore, the prediction conditions can be generated objectively, and the time and effort of a person expended for generating the prediction conditions can be saved.

Preferably, in the monitoring device, the prediction condition further includes a prediction condition generated according to service life data of components of the monitored device.

According to this configuration, prediction conditions based on transition of state data when an abnormality actually occurs, and prediction conditions based on component service life data can be used in combination. In this way, anomalies that may be caused by component wear can be more accurately predicted, allowing maintenance checks of the monitored equipment to be performed at more appropriate times.

Preferably, in the monitoring device, the status data includes outputs of various sensors in the monitored device, and the prediction conditions include prediction conditions respectively corresponding to the outputs of the various sensors.

According to this configuration, the monitoring operation is performed based on various types of status data, so that the status of the monitored equipment can be judged more correctly, so that an abnormal situation can be predicted more accurately.

Preferably, the monitoring device includes a communication part, which sends a notification that the status monitoring part predicts an abnormality to the monitored device through a communication network. Also, more preferably, the communication part transmits instructions on how to fix the anomaly.

These configurations allow the user of the monitored device to take appropriate action.

In the case where the monitored device includes a light source, and the light quantity of the light source is output as status data, in the monitoring device, the prediction condition for predicting the abnormality of the light source may be that the trend of the status data corresponding to the light quantity of the light source tends to increase in value , or the rate of change of the state data value corresponding to the light quantity of the light source deviates from a predetermined range.

That is, the light quantity of the light source may temporarily increase or become unstable until an abnormality such as lamp breakage occurs in the light source. Therefore, the above-mentioned predictive conditions can be used, whereby an abnormality of the light source in the monitored device can be detected more accurately.

In addition, in the case where the monitored device includes a part driven by a pulse motor, and the number of pulses of the pulse motor for driving the part is output as status data, in the monitoring device, the The prediction condition may be that the state data value corresponding to the pulse number of the pulse motor deviates from a predetermined range of the pulse number.

For example, in the case where the frictional resistance of a part driven by a pulse motor increases for some reason, a loss of synchronization may occur in the pulse motor, causing the part to require more pulses than normal to Execute a predetermined operation. In this way, the above-mentioned predictive conditions can be used, whereby an abnormality of such a component in the monitored equipment can be detected more accurately.

Preferably, in the monitoring device, the monitored device includes a component that repeatedly executes a predetermined operation when it cannot be successfully completed, and outputs the number of repetitions of the predetermined operation performed until it is successfully completed as status data, And the prediction condition for predicting the abnormality of the component includes: the state data value corresponding to the number of repetitions becomes higher than a value within a predetermined range. This makes it possible to more accurately detect anomalies in such a component in the monitored equipment.

In order to achieve the above object, the monitored device according to the present invention is a monitored device monitored by any one of the above-mentioned monitoring devices, which includes a sensor part that outputs status data and a device that sends the status data to the monitoring device through a communication network communication part.

According to this configuration, status data is sent to a monitoring device via a communication network, whereby abnormal situation prediction service can be received through remote monitoring. This provides monitored equipment with a high uptime and reduces maintenance costs.

Preferably, in the monitored device, the communication section transmits the status data immediately after the status data is output from the sensor section. The advantage of this is that the monitoring equipment can always keep up with the latest status of the monitored equipment.

Preferably, in this monitored equipment, there is also a storage part in order to accumulate the status data output from the sensor part, and the communication part sends the status data accumulated in the storage part at a predetermined time, the advantage of doing like this is that the data The transmission efficiency is improved.

In order to achieve the above object, a first method according to the present invention is a method for allowing a computer to execute a process, the method comprising the steps of: receiving status data representing the status of the monitored device from a monitored device; Conditions are compared with the state data, wherein the prediction condition includes a condition generated according to a change pattern indicating an abnormal precursor in the state data before the time point when the abnormality occurs in the monitored equipment; and when the state data meets the prediction condition, the monitored The device provides notifications of predicted anomalies.

The program is read into the computer for execution, thereby realizing the monitoring device according to the present invention.

In order to achieve the above object, a second method according to the present invention is a method for allowing a computer to execute a process, the method comprising the steps of: accumulating in a data storage section received status data from a monitored device; and using the The state data accumulated in the data storage section generates a condition for predicting abnormality based on a change pattern indicating a precursor of abnormality in the state data before the point in time when the abnormality occurred.

The program is read into a computer for execution, thereby realizing a monitoring device according to the present invention, which automatically generates predictive conditions based on state data obtained from an operating monitored device.

Description of drawings

FIG. 1 is a schematic block diagram showing the configuration of a monitoring system according to one embodiment of the present invention.

FIG. 2 is a diagram showing an example of a change pattern of state data of a monitored clinical testing device in the monitoring system as a basis for generating prediction conditions.

FIG. 3 is a diagram showing another example of a change pattern of the state data of the clinical testing device as a basis for generating a prediction condition.

Fig. 4 is a perspective view showing the structure of a test strip feeder included in a device as an example of a clinical testing device.

FIG. 5 is an enlarged perspective view showing the rotor shown in FIG. 4 and its peripheral portion.

FIG. 6 is a cross-sectional view taken along line X-X in FIG. 4 .

Fig. 7 is a graph showing a change pattern of a set of state data obtained from 100 measurements performed in a period of time before the point in time at which test strip sticking occurs.

FIG. 8 is a graph showing a change pattern of a set of state data obtained from 100 measurements performed in a time period preceding the 100 measurements shown in FIG. 7 .

Detailed ways

(Example 1)

Referring to the accompanying drawings, the present invention will be described below by way of an embodiment.

As shown in FIG. 1, in the configuration of a monitoring system according to the present embodiment, the host computer 100 (monitoring equipment) of an equipment manufacturer/distributor such as clinical testing equipment, through a communication network 300 such as the Internet, It is interconnected with a clinical detection device 200 (monitored device) that has been delivered to the user by the manufacturer/distributor. In FIG. 1 , for simplicity, only one clinical testing device 200 is shown. However, any number of clinical testing devices 200 may also be connected to the host computer 100 .

The communication network 300 is not limited to the above-mentioned Internet, and it may be formed of any communication medium capable of bidirectional communication. Through this communication network 300 , data representing the status of the device (hereinafter referred to as status data), error signals, failure signals, etc. are transmitted from the clinical testing device 200 to the host computer 100 . In addition, the data request signal, the content of the manual for operating the clinical testing device 200 , etc. are transmitted from the host computer 100 to the clinical testing device 200 .

In the present embodiment, "error" refers to operational abnormality mainly caused by a user's operational error, and it does not require repair or the like of the device itself. For example, "error" may be a phenomenon in which measurement is terminated because the user does not place the test paper for measurement. This situation does not require repairs, etc., since the normal state can be returned to once the user has placed the test strip. On the other hand, "failure" refers to a phenomenon in which abnormality in operation is mainly due to an abnormality generated in equipment, and repair, replacement of parts, etc. are required. The host computer 100 as a monitoring device is used to predict failure.

As shown in FIG. 1 , the host computer 100 includes a communication section 101 , a control section 102 , a data storage section 103 , a condition storage section 104 , a status monitoring section 105 and an abnormality analysis section 106 .

The communication section 101 transmits and receives these data and the like described above to and from the clinical testing device 200 through the communication network 300 . The control section 102 controls operations of various sections of the host computer 100 according to predetermined programs. The data storage section 103 stores status data transmitted from the clinical testing device 200 . The condition storage section 104 stores conditions (referred to as predicted conditions) that appear in the state data and indicate abnormality. The status monitoring section 105 monitors status data by comparing with predicted conditions to determine whether maintenance operations and the like are required for the clinical testing device 200 . In the case of a failure, the abnormality analysis section 106 analyzes a change in the status data that has occurred as a sign of the failure, using status data obtained before the point in time at which the failure occurred.

As shown in FIG. 1 , the clinical testing device 200 includes a communication section 201 , a control section 202 , component sensors 203 a · 203 b , etc., and a display section 204 . In FIG. 1 , for the sake of simplicity, only the functional blocks of the control system related to abnormality prediction are shown for the clinical detection device 200 . However, arbitrary functional blocks may also be provided, eg, for realizing the intended purpose of the clinical testing device 200 .

The communication section 201 transmits and receives these data and the like described above to and from the host computer 100 via the communication network 300 . The control section 202 controls the operations of the respective sections of the clinical testing device 200 according to a predetermined program. The component sensors 203a·203b, etc. are sensors respectively connected to at least those components that may cause abnormal operation among the constituent components of the clinical detection apparatus 200 . The component sensors 203a, 203b, etc. produce output in the form of status data representing the status of each of these components. The display section 204 displays information notifying the user of errors, failures, etc. on a screen in addition to information as reminders and the contents of the operation manual transmitted from the host computer 100 to the user.

The operation of the monitoring system when a urine analysis device is used as the clinical testing device 200 will be described below.

The urine analysis device as the clinical testing device 200 sucks a sample (urine) from a spitz tube fixed in a sample holder using a nozzle, and spots it on a test pad of a test paper. Then, the device emits light with a predetermined wavelength onto the test paper to measure the reflectance of the light, thereby measuring urine sugar, urine protein, and the like. In addition, the device has a function to measure specific gravity based on the refractive index of light irradiated on a sample in a sample holder, and a function to measure turbidity using scattered light. The device also includes a transfer section to move the sample racks in the horizontal direction, so that an automatic transfer line can be established between the device and another device, which can share a common sample rack or can be used sequentially Multiple sample racks.

Thus, as the component sensor 203, this urine analysis apparatus includes various types of sensors in corresponding parts, for example: (1) a device that counts the number of times the syringe pump is used to allow the suction pipe to perform a suction operation and outputs the number of times thus obtained Sensor, (2) sensor for determining the operating time of the air pump (diaphragm pump) and outputting the resulting time, (3) sensor for measuring the pressure in the discharge channel and outputting the resulting pressure value, (4) measuring for A sensor that measures the amount of light of the light source lamp and outputs the amount of light thus obtained, and (5) a sensor that measures the frictional resistance of the sample holder conveying portion and outputs the resulting frictional resistance.

The timing of these data respectively output from each sensor should be set arbitrarily according to the corresponding performance of each component. In addition, one component may have two or more types of component sensors 203 so that two or more types of status data can be detected. For example, in the above-mentioned syringe pump, in addition to the sensor that counts the number of times of use, there may also be a sensor as the component sensor 203, which is used to detect the pulse motor that drives the syringe vertically. Number of drive pulses to get respectively when moving down.

Under the control of the control section 202, data (status data) respectively output from the above-mentioned component sensors 203 is transmitted from the communication section 201 to the host computer 100 via the communication network 300 according to a request. Then, these data are stored into the data storage section 103 .

The status data may be sent from the clinical testing device 200 to the host computer 100 every time the status data is output from each component sensor 203 . Alternatively, the clinical detection device 200 may have a memory (not shown) for temporarily storing state data, so that after the state data has accumulated to a certain amount, the accumulated state data group can be sent. The advantage of the former method is that the host computer 100 can always keep track of the latest status of the clinical detection equipment 200 . The advantage of the latter method is that the data transfer efficiency is improved.

In the host computer 100 , the state monitoring section 105 compares the state data stored in the data storage section 103 with the predicted conditions stored in the condition storage section 104 to judge the state of the clinical detection device 200 .

As initial values of prediction conditions, conditions based on the service life data of each component and the like are set in the condition storage section 104 . For example, for the state data obtained in (1) above from a syringe pump, "the number of times of use reaches 10,000" is set as an initial value of the prediction condition. In addition, for the state data obtained in (2) above from the air pump, "use time reaches 5,000 hours" is set as an initial value of the prediction condition. For the status data obtained in (3) above from the discharge channel, "the drop in pressure in the channel is less than 60 kPa after 5 seconds from the start of pump driving" is set as an initial value. For the status data obtained in (4) above from the light source lamp for measurement, "the light quantity of the lamp falls to not more than 70% of the initial value" was set as the initial value. For the state data obtained in (5) above from the sample rack conveying section, "friction reaches not less than a certain value (N)" is set as an initial value.

In addition, in the condition storage section 104, a situation handling method is also stored in association with each of these conditions for standby. For example, "lubricate the syringe pump" and "replace the O-ring" are stored as the case processing methods for the case where the prediction condition of the syringe pump related to the above (1) is satisfied. Also, for a case where the prediction condition of the air pump related to (2) is satisfied, "replace this pump" is stored as a case processing method. For the case where the prediction condition of the discharge passage related to (3) is satisfied, "clear the blockage in the passage, or confirm that the performance of the pump is degraded to replace the pump if necessary" is stored as a situation handling method. For the case where the prediction condition of the light source lamp related to (4) is satisfied, "replace this lamp" is stored as a case processing method. For the case of satisfying the prediction condition of the sample rack conveying part related to (5), "cleaning the part causing friction" is stored as a case processing method.

The status monitoring section 105 compares status data from the clinical testing device 200 with predicted conditions. If the state data satisfies the prediction condition, the state monitoring part 105 sends, for example, information about the situation processing method stored in relation to the prediction condition in the condition storage part 104 to the clinical detection device 200 through the communication part 101 and the communication network 300 , and messages indicating warnings that unusual conditions are very likely to occur. In the clinical testing device 200 , after the communication section 201 receives these information, the display section 204 displays the situation handling method on the screen under the control of the control section 202 . In this way, the user of the clinical testing device 200 can perform self-processing of the situation based on the information displayed on the screen or the like. Or, in the case where self-processing is not effective, a maintenance person can be requested to be dispatched.

However, in practice, due to influences such as usage status, maintenance status, or condition differences when producing components, in some cases, abnormal situations may occur before the status data meets the initially set prediction conditions. Conversely, in some other cases, no abnormality occurs even after the point in time at which the state data satisfies the prediction condition.

In view of this, as will be described hereinafter, the host computer 100 according to the present embodiment refers to the status data stored in the data storage section 103 when an abnormality actually occurs. The host computer 100 then generates a new prediction condition and adds it to the condition storage section 104 based on the change pattern of the status data obtained just before the occurrence of the abnormal situation. Thereafter, the state monitoring section 105 compares the initially set prediction condition and the added prediction condition with the state data in order to monitor the clinical detection device 200 . If the state data satisfies any predictive condition therein, the state monitoring section 105 transmits the above-mentioned information and the like (ie, information such as issuing a warning and giving instruction information on the situation handling of the clinical testing device 200 ) to the clinical testing device 200 .

A method for setting new prediction conditions when an exception occurs will be explained below. The method is set according to the change pattern of the status data obtained before the abnormality occurs. An example in which the light source lamp for measurement is broken in the above urine analysis apparatus is specifically described here.

FIG. 2 is a graph showing the transition of status data from the light source lamp for measurement, that is, the light quantity of the lamp measured by any one of the component sensors 203 and sent to the host computer 100 . As shown, the lamp broke after approximately 6000 hours of initial use.

When the light source lamp breaks, the component sensor 203 for the lamp detects the failure, ie, "the light source lamp breaks", and outputs a failure signal assigned in advance to this type of failure. The failure signal is sent to the host computer 100 through the communication section 201 and the communication network 300 .

In the host computer 100, after the communication part 101 receives the fault signal, under the control of the control part 102, the abnormality analysis part 106 extracts from the data storage part 103 within a predetermined period before the time point when the fault signal is received. Obtained state data. Subsequently, the abnormality analysis section 106 analyzes the extracted status data, extracts a change pattern as an abnormality indication, and sets a new prediction condition.

For example, in the status data shown in FIG. 2 , the light quantity of the lamp as the status data gradually decreases after the lamp starts to be used. However, as can be seen from the figure, the light quantity shows a tendency to turn to increase before the lamp breaks. Therefore, "increased light quantity of lamp" is added in the condition storage section 104 as a new prediction condition. Thus, thereafter, for the light quantity of the lamp as the state data of the light source lamp, when any one of the conditions of "the light quantity of the lamp falls to not more than 70% of the initial value" and the condition of "the light quantity of the lamp increases" is satisfied, A message such as an alarm will be issued.

In addition, in the case where, for example, the light quantity of the lamp has a change pattern as shown in FIG. 3 , as can be seen from the figure, the light quantity before the lamp breakage is unstable. Thus, in this case, for example, "the rate of change of the light quantity of the lamp deviates from a predetermined range" may be newly added in the condition storage section 104 as a predictive condition.

In addition, for the above-mentioned syringe pump, for example, in the case where a failure occurs in the vertical drive of the syringe before the predicted condition set initially (i.e., before the number of uses reaches 10,000), for the pulse motor that drives the syringe to move vertically, it is possible to control the syringe. The number of drive pulses obtained separately when moving up and down is analyzed. As a result, it can be seen that a deviation of 4 or more pulses is generated before the point in time when the abnormality occurs. In this case, "the difference between the number of drive pulses obtained when the syringe of the pulse motor moves up and down reaches 4 or more" should be stored in the condition storage section 104 as a new prediction condition.

As described above, according to the configuration of the present embodiment, prediction conditions based on component service life and the like are initially set in the condition storage section 104 . When a failure actually occurs, a new predicted condition is added to the condition storage section 104 based on the change pattern of the state data obtained before the time point of occurrence of the failure. This will allow the status monitoring section 105 to more accurately detect signs of abnormality.

The new prediction condition based on the state data change pattern can be generated by the abnormal analysis part 106 according to a predetermined algorithm, or can also be created and set by people according to the change pattern extracted and analyzed by the abnormal analysis part 106 .

(Example 2)

Next, the present invention will be described by another embodiment.

In this embodiment, as an example of the clinical testing device 200, the device used is equipped with a test strip feeding section (not shown in FIG. 1) for supplying a test strip to a testing section. The following description will focus on a mechanism in which an operational abnormality of the device is predicted by the host computer 100 .

As shown in FIG. 4, a test strip feeder included in the clinical testing apparatus 200 is a mechanism for continuously supplying test strips, one at a time, to a predetermined testing section. A test strip is a thin, short piece with many different types of reagent pads placed on one surface.

As shown in Figure 4, the test piece feeder includes a base 1, a frame 2, support members 3a and 3b, a rotor 4, a storage part 5, a test piece detection block 6, an inclined cover 7, a drum 8, a base member 9, a driving part 10 and a drum control section (not shown). The drum control section is composed of a microcomputer and the like.

The supporting members 3a and 3b, the storing portion 5 and the test piece detection block 6 form the peripheral side wall of the supply portion 11 in which a plurality of test pieces are supplied. The rotor 4 takes one test piece at a time from the supply section 11 and supplies it to the drum 8 . The photoelectric sensor 6a (see FIG. 6 ) is combined inside the test piece detection block 6 . The sensor detects the sides of a test strip fed from the rotor 4 to the drum 8 .

A pulse motor 10A for supplying rotational power to the rotor 4 and the drum 8 is disposed in the drive section 10 . The drive shaft of the pulse motor 10A is coupled to the rotating shafts of the rotor 4 and the drum 8 through a drive transmission system 10B, which includes a conveyor belt, pulleys 10Ba and 10Bb, etc., some of which are not shown.

In this test strip feeder, as the part sensor 203 shown in FIG.

FIG. 5 is an enlarged perspective view showing the rotor 4 shown in FIG. 4 and its peripheral portion. Fig. 6 is a sectional view taken along line X-X of Fig. 4 . As shown in FIGS. 4 to 6, the external shape of the rotor 4 is a columnar shape generally longer than its width, and is roughly constituted by a peripheral portion 4a, a rotating shaft 4b, and a spoke member 4c. The peripheral portion 4a is formed in a cylindrical shape whose length is greater than its width, and its longitudinal length is substantially equivalent to the longitudinal length of the test piece. The rotation shaft 4b is located at the center inside the peripheral portion 4a, and it is connected to the inner side surface 4aa of the peripheral portion 4a through the spoke members 4c. Each end portion of the rotary shaft 4b is inserted into a through hole at a predetermined portion of each of the supporting members 3a and 3b. The rotor 4 is provided so as to be rotatable while being supported by a shaft between the supporting members 3a and 3b.

Simultaneously, a plurality of concave portions 4d in the shape of groove lines are formed on the outer side surface 4ab of the peripheral portion 4a, forming one turn in the direction of rotation. In addition, a groove portion 4e having a length longer than the width is formed on the outer side 4ab so that a test piece can be fitted in the groove portion 4e along the length direction perpendicular to the rotation direction.

Under the control of the driving part 10, the rotor 4 is between the position (initial position) where the groove part 4e is located under the storage part 5 and the position (judgment position) where the photoelectric sensor 6a can judge whether there is a test piece in the groove part 4e Perform reciprocating rotary motion. The rotation angle is controlled according to the number of driving pulses of the pulse motor 10A, and the rotation position of the rotor 4 is detected by the photoelectric sensor 6a. That is, when the pulse motor 10A is driven by a predetermined number of pulses (such as 500 pulses) to rotate the rotor 4 from the initial position, if the photoelectric sensor 6A detects that the rotor 4 is at the judgment position, it judges that the rotor 4 is operating normally.

When the user supplies a plurality of test pieces in the supply part 11 in longitudinal alignment, as shown in FIG. 3 , the rotor 4 rotates counterclockwise from an initial position. At this time, a test piece located at the bottom of the supply portion 11 moves toward the test piece detection block 6 as the rotor 4 rotates while being fitted in the groove portion 4e of the rotor 4 . In this case, a plurality of test pieces provided in the supply portion 11 are stacked on the outer side 4ab of the rotor 4 including the groove portion 4e. When the groove portion 4e is moved to its position facing the test piece detecting block 6, the test pieces stacked in the groove portion 4e are picked up by the separating plate 6e as only one test piece.

The rotor 4 is further rotated so that a test piece placed in the groove portion 4e passes the test piece detection block 6 and reaches the judging position while being combined with the groove portion 4e. In this position, the photoelectric sensor 6a of the test strip detection block 6 detects whether or not a test strip is placed in the groove portion 4e. When the photoelectric sensor 6a detects the test piece, that is, when it confirms that the rotor 4 has rotated to the judgment position, the driving part 10 further drives the motor 10A with a predetermined number of pulses, so that the rotor 4 rotates counterclockwise. continue to rotate. Thus, the test piece is sent out to the inclined channel 12 .

If the optical sensor 6a does not detect that the rotor 4 has rotated to the judging position after the rotor 4 has rotated 500 pulses, it is conceivable that the cause is that the test piece is stuck between the rotor 4 and the separation plate 6e, thus hindering the rotor 4. 4 rotations.

In this case, in order to take out the jammed test piece, as described above, the drive section 10 moves the rotor 4 back to the original position and restarts the rotation operation. The component sensor 203 in the drive section 10 outputs the number of times (try count) until the operation is performed until one test strip is sent to the chute 12 to the host computer 100 as status data. After this operation has been repeated for a predetermined number of times (such as 50 times), it has not been detected that the rotor 4 has rotated to the judging position, a failure signal indicating that "test paper jamming" has occurred is output from the component sensor 203 of the driving part 10, And transmit it to the host computer 100 through the communication part 201 and the communication network 300 .

In the host computer 100, after the communication part 101 receives the fault signal, under the control of the control part 102, the abnormal analysis part 106 extracts from the data storage part 103 within an appropriate period before the time point when the fault information is received. Obtained state data.

Here, it is assumed that the abnormality analysis section 106 extracts, for example, the state data obtained by performing 100 measurements (that is, the number of attempts performed to take out 100 test strips) in a period of time before the time point at which "test strip jamming" occurs. ), as the state data obtained before the point in time when the abnormality occurred. Fig. 7 shows the change pattern of this status data. In addition, the abnormality analysis section 106 extracts, from the data storage section 103, state data obtained by performing another 100 measurements in a period of time before the 100 measurements, as data in a normal state, so as to be consistent with the data obtained before the abnormality occurs. The obtained state data is compared. Fig. 8 shows a change pattern of this data in a normal state.

By comparing Fig. 7 and Fig. 8, it can be seen that in the normal state (Fig. 8), the number of attempts performed until a test piece is taken out is an average of 3 to 4 times, and just before the time point when abnormality occurs (Fig. 7), the number of attempts suddenly increased to an average of 6 to 10 times. Therefore, for example, "the average number of attempts reaches 9 times or more" can be added to the condition storage section 104 as a new prediction condition. In this way, thereafter, the status monitoring section 105 can monitor the number of attempts as status data based on the predicted condition, so that a sign that the test paper is jammed can be detected.

In addition, in the driving section 10, there is provided another type of component sensor 203 which outputs to the host computer 100 the number of feed pulses of the pulse motor 10A required for the rotation of the rotor 4 from the initial position to the judging position as status data. The state data obtained by the component sensor 203 is analyzed by the abnormality analysis section 106 . As a result, it can be found that the number of feed pulses required for rotation from the initial position to the judgment position is 500 pulses as described above under normal conditions, and the number is not less than 560 just before the point in time when the test paper jam occurs pulse.

Therefore, it is also possible to add "the number of feeding pulses of the pulse motor to not less than 550 pulses" as a new predictive condition to the condition storage section 104, thereby achieving more accurate detection of the occurrence of a test paper jam.

Just before the point in time at which the test paper jam occurs, the number of attempts performed increases, or the number of feeding pulses of the pulse motor increases, for the reasons described below. That is, the dust generated by the test piece will stick to the surface of the rotor 4 and the groove 4e, etc., so that the test piece is more likely to be stuck between the rotor 4 and the separation plate 6e, and the rotational friction resistance of the rotor 4 will increase. Therefore, it is preferable that a message giving an indication of "clean the rotor and groove portion" is transmitted from the host computer 100 to the clinical testing device 200 as a case-handling method when a sign of a test paper jam is detected. This configuration enables the user of the clinical testing device 200 to take appropriate action when signs of test strip sticking occur, thereby preventing failure, ie test strip sticking, from actually occurring.

In the above description, every time an abnormal situation occurs, a new prediction condition will be added according to the change pattern of the state data. However, when an abnormal condition occurs occasionally, such as under a specific usage condition, the change pattern of the state data obtained before the abnormal condition occurs may not necessarily provide a reliable indication of the abnormal condition. Using predictive conditions based on such a changing pattern may instead lead to a decrease in the accuracy of predicting anomalies.

Therefore, in the host computer 100, when a common failure occurs in each of a plurality of clinical testing devices 200, the state data of each clinical testing device 200 is analyzed, and, for example, only when a predetermined number or more Only when a common variation pattern is observed in the predetermined number of clinical detection devices 200 will a new prediction condition be generated according to the variation pattern. In this way, a common predictive condition for detecting a sign of an abnormality can be set, whereby the abnormality can be predicted more accurately.

In this case, for each of the plurality of clinical testing apparatuses 200, data including a production lot number, a production date, and a production lot number of parts used in each part (production-related data) may also be are stored in the data storage section 103, or in a storage section in the host computer 100 for storing these production-related data. Subsequently, when common abnormal data is detected from a plurality of clinical testing devices 200, the host computer 100 determines the production of each clinical testing device 200 in which abnormality occurs based on production-related data stored in the data storage section 103 or the like. Lot number, production lot number of parts used in the part where the abnormality occurred, etc. In addition, in the case that, for example, the production batch number of the equipment and the production batch number of the abnormal parts are the same for all or part of the multiple clinical testing equipment 200 that is abnormal, then determine those clinical testing equipment 200 that have the abnormality. The clinical testing equipment 200 with the same production batch number, or the clinical testing equipment 200 using abnormal parts with the same production batch number. In this way, for these determined clinical testing devices 200, appropriate actions can be taken to prevent failures from occurring.

The present invention is not limited to the above-described embodiments, and various modifications can be made thereto within the scope of the present invention. For example, in the above description, during the actual operation of the clinical detection device 200 by the user, status data is sent to the host computer 100 as needed, and a new prediction condition is generated after an abnormality occurs. However, in addition to such a configuration, the predictive condition may be generated based on a change pattern of state data obtained by the manufacturer/distributor of the clinical testing device 200 when an abnormality occurs in the clinical testing device 200 in which a test is run, and stored in stored in the condition storage section 104 of the host computer 100 for use.

In addition, the monitoring device according to the present invention is not limited to the host computer as described above, and it can also be realized using any computer such as a personal computer, a workstation, and the like. In addition, the monitored equipment is not limited to clinical testing equipment, and any equipment that requires testing and maintenance, such as automobiles, is also applicable to the present invention. In addition, household appliances that do not require inspection and maintenance are also applicable to the present invention. Moreover, the connection between the monitored device and the communication network is not limited to a wired connection, and mobile communication, radio connection, such as Home RF (HomeRF), Bluetooth (Bluetooth) and the like may also be used.

In each of the monitoring systems described in the above embodiments, state data is collected from devices actually used by users, and predictive conditions are generated according to change patterns of the state data. On the other hand, for example, a configuration is also possible in which an endurance test is carried out by a manufacturer of clinical testing equipment, etc. Prediction conditions are generated from state data obtained during the test. However, according to the monitoring system of the present embodiment, prediction conditions conforming to actual usage environments and usage conditions can be generated, so that abnormalities can be predicted more accurately than the case of using prediction conditions obtained based on tests.

Moreover, the monitoring system allows a plurality of clinical testing devices and the like to be connected with one monitoring device through a communication network. As described above, a configuration is also possible in which the predicted condition is generated by the manufacturer by using the testing equipment selected for the test from the equipment on the production line. However, in general, there is a limit to the number of devices that can be used in a trial. On the other hand, in the monitoring system according to the present embodiment, all users' devices can be monitored logically, and predictive conditions can be generated based on status data obtained from a plurality of devices. As a result, more general predictive conditions can be generated, which in turn can predict anomalies more accurately.

Moreover, in the monitoring equipment of the monitoring system, the suitable new prediction conditions that have been generated can be added to the condition storage part at any time, so that the longer the running time of the system, the more effective the accumulated prediction conditions can be. Anomalies are accurately detected. In this way, the following effects can also be achieved. That is, the normal operation rate of clinical testing equipment and the like can be further improved, thereby improving user satisfaction, and further suppressing an increase in maintenance cost.

Industrial applicability

As described in the foregoing discussion, according to the present invention, the status of the monitored equipment is remotely monitored, so signs of abnormal conditions can be detected, which can provide a monitoring system that performs maintenance checks at appropriate times, thereby allowing the monitored Equipment achieves a higher uptime rate while suppressing increases in maintenance costs.

Claims (12)

1. A monitoring device, comprising: The status data receiving part receives status data representing the status of the monitored device from a monitored device through a communication network; a condition storage section storing a prediction condition; and The state monitoring part, which compares the state data with the predicted conditions in the condition storage part, thereby predicting abnormal situations, The prediction condition includes a prediction condition generated according to a change pattern indicating an abnormal precursor in the state data before the time point when the abnormal situation occurs in the monitored equipment. 2. The monitoring device of claim 1, The generated prediction condition is a condition generated according to the transition of the status data having a common change pattern among the transitions of the status data observed in at least two monitored devices where a common abnormal situation occurs. 3. The monitoring device of claim 1 or 2, further comprising: a data storage section that accumulates status data received from monitored devices; and An abnormality analysis section that uses the state data accumulated in the data storage section to generate a prediction condition based on a change pattern indicating a sign of abnormality in the state data before the point in time when the abnormality occurred, and stores the prediction condition in the condition storage section middle. 4. A monitoring device as claimed in claim 1 or 2, Wherein the prediction condition further includes the prediction condition generated according to the service life data of the components of the monitored equipment. 5. A monitoring device as claimed in claim 1 or 2, wherein said status data includes the output of various sensors in the monitored equipment, and The prediction conditions include prediction conditions respectively corresponding to the outputs of the plurality of sensors. 6. The monitoring device according to claim 1 or 2, further comprising a communication section which transmits a notification that said state monitoring section predicts an abnormality to the monitored device through said communication network. 7. The monitoring device of claim 6, Wherein the communication portion sends instructions on how to fix the exception. 8. A monitoring device as claimed in claim 1 or 2, wherein said monitored device includes a component that repeatedly executes a predetermined operation when the predetermined operation cannot be successfully completed, and the number of repetitions of the predetermined operation performed until successful completion is output as said status data, and The prediction condition for predicting the abnormality of the component includes a value of the status data corresponding to the number of repetitions becoming higher than a value within a predetermined range. 9. A monitoring device as claimed in claim 1 or 2, where manufacturing-related data concerning each monitored device is stored, upon detection of a common anomaly in a plurality of monitored devices, the monitoring device consults manufacturing-related data relating to the plurality of monitored devices, and Where there is a piece of manufacturing-related data common to at least a portion of the plurality of monitored devices, the monitoring device determines the monitored device to which the manufacturing-related data applies, rather than the plurality of monitored devices at least part of . 10. The monitoring device of claim 9, The data related to manufacturing includes the production batch number identifier of the monitored equipment, the production date of the monitored equipment, the production batch number identifier of a part of the monitored equipment, and the production date of the part of the monitored equipment. At least one of the data selected in the group. 11. A method of allowing a computer to perform a process, the method comprising the steps of: receiving status data from a monitored device representing the status of the monitored device; comparing a predicted condition with the status data, wherein the predicted condition includes a condition generated based on a pattern of change in the status data prior to a point in time when the abnormality occurred in the monitored equipment, indicating a precursor to the abnormality; and When the state data meets the prediction condition, a notification of a predicted abnormality is provided to the monitored device. 12. A method for allowing a computer to perform a process, the method comprising the steps of: accumulating status data received from a monitored device in a data storage section; and Using the state data accumulated in the data storage section, a condition for predicting abnormality is generated from a change pattern indicating a precursor of abnormality in the state data before the time point at which the abnormality occurred.

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